Multimodal Deep Learning: Efficiency and Personalization

Multimodal Deep Learning: Advancing Language and Visual Understanding

Recent advancements in multimodal deep learning have significantly enhanced the integration and interaction between language and visual data. The field is witnessing a shift towards more efficient and scalable models that can continually evolve to incorporate new modalities without extensive retraining. This approach not only reduces computational burdens but also improves the robustness and adaptability of multimodal models. Key innovations include the development of frameworks that enable continual learning across modalities, enhancing both visual understanding and linguistic performance. Additionally, there is a growing focus on personalized applications, such as personalized sticker retrieval, which leverage advanced vision-language models to better capture user-specific semantics and preferences.

Noteworthy contributions include:

  • A method that significantly reduces linguistic performance degradation in multimodal models by up to 15% while maintaining high multimodal accuracy.
  • A scalable framework that allows multimodal models to expand to new modalities using uni-modal data, reducing training burdens by nearly 99%.
  • A personalized sticker retrieval system that outperforms existing methods in multi-modal retrieval tasks.

Sources

Improving Multimodal Large Language Models Using Continual Learning

LLMs Can Evolve Continually on Modality for X-Modal Reasoning

What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration

PerSRV: Personalized Sticker Retrieval with Vision-Language Model

Using Multimodal Deep Neural Networks to Disentangle Language from Visual Aesthetics

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